An analyst’s job involves a lot of data. So we need to understand the tools analysts require background knowledge of data. Any type of data can be used for analytics for data capture, data analysis and data provisioning.
- Data Capture
Devices, processes and people produce a lot of data. This determines discreteness and the method that the analysts use to extract the data that has been extracted.
- Data Provisioning
To produce results the analysts need data from multiple sources. For this the team needs:
- EMR data for clinical and lab results.
- Billing data for diagnosis and procedures charges.
- Cost data for improvement on margins.
- Patient satisfaction data.
It is very hard to put all the data into a single spot and one must ensure that this data set must communicate with one another. This pulling of data from data sets also makes data susceptible to errors and redundancies.
- Data Analysis
Once we get the appropriate data, and it is tied together then we can start the data analysis. It involves:
- Data quality evaluation:Data must be properly evaluated. By this way they need to find the method of evaluation.
- Data discovery:Analyst must try to explore data.
- Interpretation:It is the most important step after analyzing the data. But this is just a sub process for the same in the long process for the data analysts. One must devote time for analyzing the data and finding effective conclusions.
- Presentation:Presentation is the most critical process. After the completion of the work, the analyst must try to show data in a consumable manner for the audience to conclude the result for the data.
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